Template matching approach to content based image indexing by low dimensional Euclidean embedding

Author

Schweitzer, Haim

Author_Institution

Texas Univ., Dallas, TX, USA

Volume

2

fYear

2001

fDate

2001

Firstpage

566

Abstract

Content based indexing is computed from input that consists of matching values between images and templates. The key idea is to embed both images and templates in a low-dimensional Euclidean space so that matching between embedded images and embedded templates approximates the given input. It is shown that such embedding can be computed by means of a singular value decomposition of the input matrix. Classic principal component analysis is shown to be a special case of the proposed technique, corresponding to the case where the templates and the images are the same

Keywords

content-based retrieval; database indexing; image matching; principal component analysis; singular value decomposition; content based image indexing; content based indexing; embedded images; embedded templates; images; low dimensional Euclidean embedding; low-dimensional Euclidean space; principal component analysis; singular value decomposition; template matching approach; templates; Application software; Computer applications; Covariance matrix; Embedded computing; Indexing; Principal component analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7695-1143-0

Type

conf

DOI

10.1109/ICCV.2001.937676

Filename

937676